Digital Twin-Based Federated Transfer Learning for Anomaly Detection in Industrial IoT

Anomaly detection is increasingly becoming crucial for maintaining the safety, reliability, and efficiency of industrial systems. Recently, with the advent of data-driven decisionmaking, several statistical and machine-learning methods have been proposed. However, these methods face several challenges, such as dependence on only real sensor datasets, limited labeled data, high false alarm rates, and privacy concerns due to centralized data processing. To address these issues, we propose a digital twin-based anomaly detection approach using federated transfer learning. Digital twins allow effective anomaly detection without requiring extensive real-world failure data. The integration of digital twins with federated learning offers a powerful solution to the challenges of anomaly detection in industrial systems. The proposed method integrates digital twin data for initial training with real physical system data for model refinement. Federated learning enhances this process by maintaining data privacy through the sharing of model updates instead of raw data. The proposed combination improves model generalization, training efficiency, and performance while ensuring data privacy. We perform an extensive analysis using publicly available datasets from real-world digital and physical asset counterparts. The results demonstrate significant improvements in anomaly detection performance, highlighting the effectiveness of integrating digital twins with federated learning.